Three-Level Learning
Python for Data Analysis
“Python for Data Analysis” was the first course in a flagship certificate program at SkillFactory, a leading IT training provider. Despite high enrollment, learner feedback was poor. I led a full redesign to address skill gaps, recognize prior knowledge, and support diverse learner profiles through adaptive paths and performance-focused practice.
1. Project Overview
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GoalRedesign a popular but underperforming course to better support diverse learner needs and improve outcomes -
ID ApproachUnderstanding by Design, ADDIE, Four-Component Instructional Design (4C/ID) -
Audience25 - 45 year olds who want to use Python programming in data analysis -
ToolsOpen edX LMS, Zoom, Slack, Google Colab, automatic code grader
2. Why This Project?
Python is widely used in both software development and data analysis. For aspiring data scientists, learning Python is often the first step.
Although “Python for Data Analysis” was a top-seller and part of SkillFactory’s Data Science Certificate, it hadn’t been built with data science applications in mind. Learner satisfaction was low, and feedback indicated poor alignment with their goals.
Redesigning this foundational course was essential to improve learner outcomes and strengthen the reputation of the broader program.
Although “Python for Data Analysis” was a top-seller and part of SkillFactory’s Data Science Certificate, it hadn’t been built with data science applications in mind. Learner satisfaction was low, and feedback indicated poor alignment with their goals.
Redesigning this foundational course was essential to improve learner outcomes and strengthen the reputation of the broader program.
Python is widely used in both software development and data analysis. For aspiring data scientists, learning Python is often the first step.
Although “Python for Data Analysis” was a top-seller and part of SkillFactory’s Data Science Certificate, it hadn’t been built with data science applications in mind. Learner satisfaction was low, and feedback indicated poor alignment with their goals.
Redesigning this foundational course was essential to improve learner outcomes and strengthen the reputation of the broader program.
Although “Python for Data Analysis” was a top-seller and part of SkillFactory’s Data Science Certificate, it hadn’t been built with data science applications in mind. Learner satisfaction was low, and feedback indicated poor alignment with their goals.
Redesigning this foundational course was essential to improve learner outcomes and strengthen the reputation of the broader program.
3. Needs Analysis
Performance Objectives
I conducted discovery sessions with SMEs to define the core Python skills needed for data science. These informed curriculum updates, benchmarked against student feedback and market competitors.
Learner Segments
I discovered that the course had three distinct types of learners:
- EntryComplete beginners who need detailed instruction and practice support
- BasicLearners with some skills who need an option to skip what they already know
- IntermediateLearners who have most required skills but need to close specific gaps
Old Course Audit
With the skillset and learner types defined, I audited the original course and identified the following issues:
- No Recognition of Prior LearningBasic- and intermediate-level learners couldn’t skip what they already knew
- One Difficulty LevelUniform content didn’t serve varied experience levels
- Inadequate PracticeInsufficient activities lacked relevance and real-world application
- Irrelevant Course ContentCourse wasn’t aligned with data science roles
The audit confirmed the need for a redesign that addressed performance outcomes and the needs of all three learner types.
4. Instructional Design Strategy
To address learner diversity and align the course with program goals, I applied a learner-centered, performance-focused strategy:
- Skill DiagnosticsIntroduced pre- and post-module assessments to enable adaptive pathways and track progress
- Three-Level Practice ModelDesigned scaffolded real-world assignments at three difficulty levels to personalize learning and build mastery
- Relevance by DesignPartnered with SMEs to align content with real-world job tasks and the target skillset
- Integrated SupportCombined self-paced modules with automated feedback, live webinars, and peer discussion to drive engagement and retention
5. Development Highlights
I managed content and assessment development across five modules to ensure alignment with learning outcomes, instructional strategy, and learner needs.
SME Collaboration
I recruited and coordinated three SMEs to create content and assessments. I also onboarded a new webinar instructor to improve live learner support.
Assessments
Here’s how we addressed prior learning, varied skill levels, and the need for more practice:
- 15questions in diagnostic tests in each module to support personalized learning
- 450formative assignments across three difficulty levels to build confidence and fluency
- 47summative assignments to support performance-based assessment
Instructional Assets
We redesigned course materials to better support skill development:
- Step-by-step TutorialsExplanations of Python fundamentals for beginners
- Video Lessons15 screencasts to explain complex topics across modules
- Additional ResourcesLinks to external materials for deeper learning
6. Evaluation
The redesigned course received strong feedback from both stakeholders and learners, with particular praise for its clarity, structure, and practical relevance:
I don't think we've had such a thoughtful approach to the design of any of our courses.
The modules are well structured and the assignments are interesting.
Compared to the new modules, the old ones seem confusing, without the necessary explanations or practice.
I liked the number of assignments and their variety; they helped me practice what I've learned.
I don't think we've had such a thoughtful approach to the design of any of our courses.
The modules are well structured and the assignments are interesting.
Compared to the new modules, the old ones seem confusing, without the necessary explanations or practice.
I liked the number of assignments and their variety; they helped me practice what I've learned.
While formal metrics were still pending, early indicators pointed to higher satisfaction, increased engagement, and stronger alignment with learners’ skill levels.
7. Project Reflection
Shifting from a one-size-fits-all model to a learner-segmented, multi-pathway design was ambitious and rewarding. Developing 450 tiered assignments required extensive coordination and effort.
In hindsight, an agile, iterative rollout—starting with one level of practice and scaling based on learner feedback—would have been more efficient. This experience reinforced the value of rapid prototyping and user testing.
This project strengthened my capabilities in adaptive course design, performance-based assessment, and SME collaboration—skills I bring to every learning initiative.
In hindsight, an agile, iterative rollout—starting with one level of practice and scaling based on learner feedback—would have been more efficient. This experience reinforced the value of rapid prototyping and user testing.
This project strengthened my capabilities in adaptive course design, performance-based assessment, and SME collaboration—skills I bring to every learning initiative.
7. Project Reflection
Shifting from a one-size-fits-all model to a learner-segmented, multi-pathway design was ambitious and rewarding. Developing 450 tiered assignments required extensive coordination and effort.
In hindsight, an agile, iterative rollout—starting with one level of practice and scaling based on learner feedback—would have been more efficient. This experience reinforced the value of rapid prototyping and user testing.
This project strengthened my capabilities in adaptive course design, performance-based assessment, and SME collaboration—skills I bring to every learning initiative.
In hindsight, an agile, iterative rollout—starting with one level of practice and scaling based on learner feedback—would have been more efficient. This experience reinforced the value of rapid prototyping and user testing.
This project strengthened my capabilities in adaptive course design, performance-based assessment, and SME collaboration—skills I bring to every learning initiative.
8. Questions?
I’d be happy to share more about this redesign process or collaborate on improving your organization’s learning outcomes through instructional design.
Photos: Christina wocintechchat.com, Luis Gomes, Christopher Gower, Gordon Cowie, Kelly Sikkema, Theo Sunardjaya